#Importing the liabraries
import warnings
import ydata_profiling as pf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import r2_score,confusion_matrix,mean_squared_error,accuracy_score,classification_report

#Import Data
pdp=pd.read_csv("parkinsons.data")
display(pdp.head())

#Automatic Exploratory Data Analysis
display(pf.ProfileReport(pdp))

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

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#Manual Exploratory Data Analysis(EDA)
print(pdp.describe())
print(pdp.info())
print(pdp.isnull().sum())
print(pdp.dtypes)
print(pdp.columns)

       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \
count   195.000000    195.000000    195.000000      195.000000   
mean    154.228641    197.104918    116.324631        0.006220   
std      41.390065     91.491548     43.521413        0.004848   
min      88.333000    102.145000     65.476000        0.001680   
25%     117.572000    134.862500     84.291000        0.003460   
50%     148.790000    175.829000    104.315000        0.004940   
75%     182.769000    224.205500    140.018500        0.007365   
max     260.105000    592.030000    239.170000        0.033160   

       MDVP:Jitter(Abs)    MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \
count        195.000000  195.000000  195.000000  195.000000    195.000000   
mean           0.000044    0.003306    0.003446    0.009920      0.029709   
std            0.000035    0.002968    0.002759    0.008903      0.018857   
min            0.000007    0.000680    0.000920    0.002040      0.009540   
25%            0.000020    0.001660    0.001860    0.004985      0.016505   
50%            0.000030    0.002500    0.002690    0.007490      0.022970   
75%            0.000060    0.003835    0.003955    0.011505      0.037885   
max            0.000260    0.021440    0.019580    0.064330      0.119080   

       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \
count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   
mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   
std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   
min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   
25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   
50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   
75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   
max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   

             RPDE         DFA     spread1     spread2          D2         PPE  
count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  
mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  
std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  
min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  
25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  
50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  
75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  
max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  

[8 rows x 23 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB
None
name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64
name                 object
MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object
Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
       'spread1', 'spread2', 'D2', 'PPE'],
      dtype='object')

#Histogram for status column
status=pdp.status
status

0      1
1      1
2      1
3      1
4      1
      ..
190    0
191    0
192    0
193    0
194    0
Name: status, Length: 195, dtype: int64

plt.hist(pdp.status)
plt.xlabel("Status")
plt.ylabel("Frequencies")
plt.show()

#BAR PLOT
plt.figure(figsize=(10, 6))
plt.bar(pdp.status,pdp.HNR)
plt.xlabel("STATUS")
plt.ylabel("HNR")
plt.title("Status Vs HNR")
plt.show()

plt.figure(figsize=(10, 6))
plt.bar(pdp.status,pdp.RPDE)
plt.xlabel("STATUS")
plt.ylabel("RPDE")
plt.title("Status Vs RPDE")
plt.show()

#Distribution PLot
warnings.filterwarnings('ignore')
rows=3
col=3
columns=pdp.columns
fig, ax=plt.subplots(nrows=rows,ncols=col,figsize=(16,14))
index=1
for i in range(rows):
    for j in range(col):
        sns.distplot(pdp[columns[index]],ax=ax[i][j])
        index=index+1    
plt.tight_layout()
plt.show()

#First 3 rows
pdp.head(3)

#Correlation
dfc=pdp.iloc[:,1:]
corr = dfc.corr()
display (corr)

#Heatmap on the above data
plt.figure(figsize=(35,30))
sns.set(font_scale=1.7)
sns.heatmap(corr,annot=True)
plt.show()

#MODEL PREDICTIONS

#Dropping name column
data=pdp.drop(columns='name')
data

#X and Y values
x=data.drop(labels=['status'],axis=1)
print(x.shape)
y=data.status
print(y.shape)

(195, 22)
(195,)

#Splitting train and test data
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.8)
print(x.shape,y.shape)
print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)

(195, 22) (195,)
(39, 22) (156, 22) (39,) (156,)

lr=LogisticRegression()
lr.fit(x_train,y_train)

LogisticRegression()

LogisticRegression()

y_pred_train=lr.predict(x_train)
print(y_pred_train)
print(y_pred_train.shape)
y_pred_test=lr.predict(x_test)
print(y_pred_test)
print(y_pred_test.shape)

[0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1
 1 1]
(39,)
[0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1
 0 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1
 0 1 1 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1
 1 1 0 0 1 1 0 0]
(156,)

#Accuracy of Model
accuracy_train=accuracy_score(y_train,y_pred_train)*100
print("Accuracy for logistic regression on train data is: ",accuracy_train)
accuracy_test=accuracy_score(y_test,y_pred_test)*100
print("Accuracy for logistic regression on test data is: ",accuracy_test)

Accuracy for logistic regression on train data is:  94.87179487179486
Accuracy for logistic regression on test data is:  82.6923076923077

#Confusion Matrix
print("confusion_matrix train is:\n ", confusion_matrix(y_train, y_pred_train))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, y_pred_test))

confusion_matrix train is:
  [[ 8  2]
 [ 0 29]]
confusion_matrix test is:
  [[ 25  13]
 [ 14 104]]

#Classification report
print('\nClassification Report Train is ')
print(classification_report (y_train, y_pred_train))
print('\nClassification Report Test is ')
print(classification_report (y_test, y_pred_test))

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      0.80      0.89        10
           1       0.94      1.00      0.97        29

    accuracy                           0.95        39
   macro avg       0.97      0.90      0.93        39
weighted avg       0.95      0.95      0.95        39


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.64      0.66      0.65        38
           1       0.89      0.88      0.89       118

    accuracy                           0.83       156
   macro avg       0.76      0.77      0.77       156
weighted avg       0.83      0.83      0.83       156

#No. of wrong predictions
print((y_test !=y_pred_test).sum(),'/',((y_test ==y_pred_test).sum()+(y_test !=y_pred_test).sum()))
print('-'*50)
print((y_train !=y_pred_train).sum(),'/',((y_train ==y_pred_train).sum()+(y_train !=y_pred_train).sum()))
print('-'*50)

27 / 156
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2 / 39
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#Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,y_pred_test))

KappaScore is:  0.5344827586206897

#PLOT
sns.regplot(x=y_test,y=y_pred_test,scatter_kws={"color":'b'},logistic=True)
plt.show()

#Model Fitting
rfc=RandomForestClassifier()
rfc.fit(x_train,y_train)

RandomForestClassifier()

RandomForestClassifier()

#Prediction
y_pred1_train=rfc.predict(x_train)
print(y_pred1_train)
y_pred1_test=rfc.predict(x_test)
print(y_pred1_test)

[0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1
 1 1]
[0 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 1 0 1
 0 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 0
 1 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0
 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 1 0
 1 1 0 0 1 1 0 0]

#Accuracy of Model
accuracy_train1=accuracy_score(y_train,y_pred1_train)*100
print("Accuracy for Random Forest Classifier on train data is: ",accuracy_train1)
accuracy_test1=accuracy_score(y_test,y_pred1_test)*100
print("Accuracy for Random Forest Classifier on test data is: ",accuracy_test1)

Accuracy for Random Forest Classifier on train data is:  100.0
Accuracy for Random Forest Classifier on test data is:  76.28205128205127

#Confusion Matrix
print("confusion_matrix train is:\n ", confusion_matrix(y_train, y_pred1_train))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, y_pred1_test))

confusion_matrix train is:
  [[10  0]
 [ 0 29]]
confusion_matrix test is:
  [[33  5]
 [32 86]]

#Classification Report
print('\nClassification Report Train is ')
print(classification_report (y_train, y_pred1_train))
print('\nClassification Report Test is ')
print(classification_report (y_test, y_pred1_test))

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00        29

    accuracy                           1.00        39
   macro avg       1.00      1.00      1.00        39
weighted avg       1.00      1.00      1.00        39


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.51      0.87      0.64        38
           1       0.95      0.73      0.82       118

    accuracy                           0.76       156
   macro avg       0.73      0.80      0.73       156
weighted avg       0.84      0.76      0.78       156

#PLOT
from sklearn.tree import plot_tree
plt.figure(figsize=(20, 10))
plot_tree(rfc.estimators_[0], filled=True)
plt.title('Random Forest Visualization')
plt.show()

#Wrong Predictions
print((y_test !=y_pred1_test).sum(),'/',((y_test ==y_pred1_test).sum()+(y_test !=y_pred1_test).sum()))
print('-'*50)
print((y_train !=y_pred1_train).sum(),'/',((y_train ==y_pred1_train).sum()+(y_train !=y_pred1_train).sum()))
print('-'*50)

37 / 156
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0 / 39
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#KAppa Score
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,y_pred1_test))

KappaScore is:  0.48130841121495327

dtc=DecisionTreeClassifier()
dtc.fit(x_train,y_train)

DecisionTreeClassifier()

DecisionTreeClassifier()

y_pred2_train=dtc.predict(x_train)
print(y_pred1_train)
y_pred2_test=dtc.predict(x_test)
print(y_pred1_test)

[0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1
 1 1]
[0 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 1 0 1
 0 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 0
 1 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0
 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 0 1 1 0
 1 1 0 0 1 1 0 0]

#Accuracy
accuracy_train2=accuracy_score(y_train,y_pred2_train)*100
print("Accuracy for Decision Tree Classifier on train data is: ",accuracy_train2)
accuracy_test2=accuracy_score(y_test,y_pred2_test)*100
print("Accuracy for Decision Tree Classifier on test data is: ",accuracy_test2)

Accuracy for Decision Tree Classifier on train data is:  100.0
Accuracy for Decision Tree Classifier on test data is:  76.28205128205127

#Classification Report
print('\nClassification Report Train is ')
print(classification_report (y_train, y_pred2_train))
print('\nClassification Report Test is ')
print(classification_report (y_test, y_pred2_test))

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00        29

    accuracy                           1.00        39
   macro avg       1.00      1.00      1.00        39
weighted avg       1.00      1.00      1.00        39


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.51      0.76      0.61        38
           1       0.91      0.76      0.83       118

    accuracy                           0.76       156
   macro avg       0.71      0.76      0.72       156
weighted avg       0.81      0.76      0.78       156

#Wrong Predictions
print((y_test !=y_pred2_test).sum(),'/',((y_test ==y_pred2_test).sum()+(y_test !=y_pred2_test).sum()))
print('-'*50)
print((y_train !=y_pred2_train).sum(),'/',((y_train ==y_pred2_train).sum()+(y_train !=y_pred2_train).sum()))
print('-'*50)

37 / 156
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0 / 39
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#Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,y_pred2_test))

KappaScore is:  0.4496567505720823

#Plot
class_names = pdp['status'].unique()
plt.figure(figsize=(20, 20)) 
plot_tree(dtc, feature_names=x.columns,class_names=str(class_names), filled=True)
plt.title('Decision Tree Visualization')
plt.show()

knn=KNeighborsClassifier()
knn.fit(x_train,y_train)

KNeighborsClassifier()

KNeighborsClassifier()

#Prediction
y_pred3_train=knn.predict(x_train)
print(y_pred3_train)
y_pred3_test=knn.predict(x_test)
print(y_pred3_test)

[1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1
 1 1]
[1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1
 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
 1 1 0 1 1 1 1 0]

#Acccuracy
accuracy_train3=accuracy_score(y_train,y_pred3_train)*100
print("Accuracy for KNN classifier on train data is: ",accuracy_train3)
accuracy_test3=accuracy_score(y_test,y_pred3_test)*100
print("Accuracy for KNN Classifier on test data is: ",accuracy_test3)

Accuracy for KNN classifier on train data is:  76.92307692307693
Accuracy for KNN Classifier on test data is:  82.05128205128204

#Wrong Predictions
print((y_test !=y_pred3_test).sum(),'/',((y_test ==y_pred3_test).sum()+(y_test !=y_pred3_test).sum()))
print('-'*50)
print((y_train !=y_pred3_train).sum(),'/',((y_train ==y_pred3_train).sum()+(y_train !=y_pred3_train).sum()))
print('-'*50)

28 / 156
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9 / 39
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#Kappa score
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,y_pred3_test))

KappaScore is:  0.3802497162315551

svc=SVC()
svc.fit(x_train,y_train)

SVC()

SVC()

y_pred4_train=svc.predict(x_train)
print(y_pred3_train)
y_pred4_test=svc.predict(x_test)
print(y_pred3_test)

[1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1
 1 1]
[1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1
 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
 1 1 0 1 1 1 1 0]

#Accuracy
accuracy_train4=accuracy_score(y_train,y_pred4_train)*100
print("Accuracy for KNN classifier on train data is: ",accuracy_train4)
accuracy_test4=accuracy_score(y_test,y_pred4_test)*100
print("Accuracy for KNN Classifier on test data is: ",accuracy_test4)

Accuracy for KNN classifier on train data is:  74.35897435897436
Accuracy for KNN Classifier on test data is:  75.64102564102564

#Wrong Predictions
print((y_test !=y_pred4_test).sum(),'/',((y_test ==y_pred4_test).sum()+(y_test !=y_pred4_test).sum()))
print('-'*50)
print((y_train !=y_pred4_train).sum(),'/',((y_train ==y_pred4_train).sum()+(y_train !=y_pred4_train).sum()))
print('-'*50)

38 / 156
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10 / 39
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#Kappa Score
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,y_pred4_test))

KappaScore is:  0.0

FROM ALL ABOVE MODELS WE CAN TELL THAT RANDOM FOREST CLASSIFIER HAS MORE ACCURACY PREDICTING THE STATUS OF THE DISEASE

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
MDVP:Fo(Hz)	1.000000	0.400985	0.596546	-0.118003	-0.382027	-0.076194	-0.112165	-0.076213	-0.098374	-0.073742	...	-0.094732	-0.021981	0.059144	-0.383535	-0.383894	-0.446013	-0.413738	-0.249450	0.177980	-0.372356
MDVP:Fhi(Hz)	0.400985	1.000000	0.084951	0.102086	-0.029198	0.097177	0.091126	0.097150	0.002281	0.043465	...	-0.003733	0.163766	-0.024893	-0.166136	-0.112404	-0.343097	-0.076658	-0.002954	0.176323	-0.069543
MDVP:Flo(Hz)	0.596546	0.084951	1.000000	-0.139919	-0.277815	-0.100519	-0.095828	-0.100488	-0.144543	-0.119089	...	-0.150737	-0.108670	0.210851	-0.380200	-0.400143	-0.050406	-0.394857	-0.243829	-0.100629	-0.340071
MDVP:Jitter(%)	-0.118003	0.102086	-0.139919	1.000000	0.935714	0.990276	0.974256	0.990276	0.769063	0.804289	...	0.746635	0.906959	-0.728165	0.278220	0.360673	0.098572	0.693577	0.385123	0.433434	0.721543
MDVP:Jitter(Abs)	-0.382027	-0.029198	-0.277815	0.935714	1.000000	0.922911	0.897778	0.922913	0.703322	0.716601	...	0.697170	0.834972	-0.656810	0.338653	0.441839	0.175036	0.735779	0.388543	0.310694	0.748162
MDVP:RAP	-0.076194	0.097177	-0.100519	0.990276	0.922911	1.000000	0.957317	1.000000	0.759581	0.790652	...	0.744919	0.919521	-0.721543	0.266668	0.342140	0.064083	0.648328	0.324407	0.426605	0.670999
MDVP:PPQ	-0.112165	0.091126	-0.095828	0.974256	0.897778	0.957317	1.000000	0.957319	0.797826	0.839239	...	0.763592	0.844604	-0.731510	0.288698	0.333274	0.196301	0.716489	0.407605	0.412524	0.769647
Jitter:DDP	-0.076213	0.097150	-0.100488	0.990276	0.922913	1.000000	0.957319	1.000000	0.759555	0.790621	...	0.744901	0.919548	-0.721494	0.266646	0.342079	0.064026	0.648328	0.324377	0.426556	0.671005
MDVP:Shimmer	-0.098374	0.002281	-0.144543	0.769063	0.703322	0.759581	0.797826	0.759555	1.000000	0.987258	...	0.987626	0.722194	-0.835271	0.367430	0.447424	0.159954	0.654734	0.452025	0.507088	0.693771
MDVP:Shimmer(dB)	-0.073742	0.043465	-0.119089	0.804289	0.716601	0.790652	0.839239	0.790621	0.987258	1.000000	...	0.963202	0.744477	-0.827805	0.350697	0.410684	0.165157	0.652547	0.454314	0.512233	0.695058
Shimmer:APQ3	-0.094717	-0.003743	-0.150747	0.746625	0.697153	0.744912	0.763580	0.744894	0.987625	0.963198	...	1.000000	0.716207	-0.827123	0.347617	0.435242	0.151124	0.610967	0.402243	0.467265	0.645377
Shimmer:APQ5	-0.070682	-0.009997	-0.101095	0.725561	0.648961	0.709927	0.786780	0.709907	0.982835	0.973751	...	0.960072	0.658080	-0.813753	0.351148	0.399903	0.213873	0.646809	0.457195	0.502174	0.702456
MDVP:APQ	-0.077774	0.004937	-0.107293	0.758255	0.648793	0.737455	0.804139	0.737439	0.950083	0.960977	...	0.896647	0.694019	-0.800407	0.364316	0.451379	0.157276	0.673158	0.502188	0.536869	0.721694
Shimmer:DDA	-0.094732	-0.003733	-0.150737	0.746635	0.697170	0.744919	0.763592	0.744901	0.987626	0.963202	...	1.000000	0.716215	-0.827130	0.347608	0.435237	0.151132	0.610971	0.402223	0.467261	0.645389
NHR	-0.021981	0.163766	-0.108670	0.906959	0.834972	0.919521	0.844604	0.919548	0.722194	0.744477	...	0.716215	1.000000	-0.714072	0.189429	0.370890	-0.131882	0.540865	0.318099	0.470949	0.552591
HNR	0.059144	-0.024893	0.210851	-0.728165	-0.656810	-0.721543	-0.731510	-0.721494	-0.835271	-0.827805	...	-0.827130	-0.714072	1.000000	-0.361515	-0.598736	-0.008665	-0.673210	-0.431564	-0.601401	-0.692876
status	-0.383535	-0.166136	-0.380200	0.278220	0.338653	0.266668	0.288698	0.266646	0.367430	0.350697	...	0.347608	0.189429	-0.361515	1.000000	0.308567	0.231739	0.564838	0.454842	0.340232	0.531039
RPDE	-0.383894	-0.112404	-0.400143	0.360673	0.441839	0.342140	0.333274	0.342079	0.447424	0.410684	...	0.435237	0.370890	-0.598736	0.308567	1.000000	-0.110950	0.591117	0.479905	0.236931	0.545886
DFA	-0.446013	-0.343097	-0.050406	0.098572	0.175036	0.064083	0.196301	0.064026	0.159954	0.165157	...	0.151132	-0.131882	-0.008665	0.231739	-0.110950	1.000000	0.195668	0.166548	-0.165381	0.270445
spread1	-0.413738	-0.076658	-0.394857	0.693577	0.735779	0.648328	0.716489	0.648328	0.654734	0.652547	...	0.610971	0.540865	-0.673210	0.564838	0.591117	0.195668	1.000000	0.652358	0.495123	0.962435
spread2	-0.249450	-0.002954	-0.243829	0.385123	0.388543	0.324407	0.407605	0.324377	0.452025	0.454314	...	0.402223	0.318099	-0.431564	0.454842	0.479905	0.166548	0.652358	1.000000	0.523532	0.644711
D2	0.177980	0.176323	-0.100629	0.433434	0.310694	0.426605	0.412524	0.426556	0.507088	0.512233	...	0.467261	0.470949	-0.601401	0.340232	0.236931	-0.165381	0.495123	0.523532	1.000000	0.480585
PPE	-0.372356	-0.069543	-0.340071	0.721543	0.748162	0.670999	0.769647	0.671005	0.693771	0.695058	...	0.645389	0.552591	-0.692876	0.531039	0.545886	0.270445	0.962435	0.644711	0.480585	1.000000

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	0.405	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	0.263	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	0.190	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569